





import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


lowercase_         =						"""true"""




def      __UpperCamelCase    (_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=82						,		_SCREAMING_SNAKE_CASE=16						)							->  Union[str, Any]:
  set_seed(42						)
  lowercase__           =    RegressionModel()
  lowercase__           =    deepcopy(_SCREAMING_SNAKE_CASE						)
  lowercase__           =    RegressionDataset(length=_SCREAMING_SNAKE_CASE						)
  lowercase__           =    DataLoader(_SCREAMING_SNAKE_CASE						,		batch_size=_SCREAMING_SNAKE_CASE						)
  model.to(accelerator.device						)
  lowercase__     ,       lowercase__           =    accelerator.prepare(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						)
  return model, ddp_model, dataloader




def      __UpperCamelCase    (_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=False						)							->  str:
  lowercase__           =    AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'						)
  lowercase__           =    load_dataset('glue'						,		'mrpc'						,		split='validation'						)

  def tokenize_function(_SCREAMING_SNAKE_CASE						):
    lowercase__           =    tokenizer(examples['sentence1']						,		examples['sentence2']						,		truncation=_SCREAMING_SNAKE_CASE						,		max_length=_SCREAMING_SNAKE_CASE						)
    return outputs

  with accelerator.main_process_first():
    lowercase__           =    dataset.map(
        _SCREAMING_SNAKE_CASE						,		batched=_SCREAMING_SNAKE_CASE						,		remove_columns=['idx', 'sentence1', 'sentence2']						,		)

  lowercase__           =    tokenized_datasets.rename_column('label'						,		'labels'						)

  def collate_fn(_SCREAMING_SNAKE_CASE						):
    if use_longest:
      return tokenizer.pad(_SCREAMING_SNAKE_CASE						,		padding='longest'						,		return_tensors='pt'						)
    return tokenizer.pad(_SCREAMING_SNAKE_CASE						,		padding='max_length'						,		max_length=128						,		return_tensors='pt'						)

  return DataLoader(_SCREAMING_SNAKE_CASE						,		shuffle=_SCREAMING_SNAKE_CASE						,		collate_fn=_SCREAMING_SNAKE_CASE						,		batch_size=16						)




def      __UpperCamelCase    (_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						)							->  Optional[Any]:
  lowercase__           =    Accelerator(dispatch_batches=_SCREAMING_SNAKE_CASE						,		split_batches=_SCREAMING_SNAKE_CASE						)
  lowercase__           =    get_dataloader(_SCREAMING_SNAKE_CASE						,		not dispatch_batches						)
  lowercase__           =    AutoModelForSequenceClassification.from_pretrained(
      'hf-internal-testing/mrpc-bert-base-cased'						,		return_dict=_SCREAMING_SNAKE_CASE						)
  lowercase__     ,       lowercase__           =    accelerator.prepare(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						)
  return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator




def      __UpperCamelCase    (_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						)							->  Dict:
  lowercase__           =    []
  for batch in dataloader:
    lowercase__     ,       lowercase__           =    batch.values()
    with torch.no_grad():
      lowercase__           =    model(_SCREAMING_SNAKE_CASE						)
      lowercase__     ,       lowercase__           =    accelerator.gather_for_metrics((logit, target)						)
      logits_and_targets.append((logit, target)						)
  lowercase__     ,       lowercase__           =    [], []
  for logit, targ in logits_and_targets:
    logits.append(_SCREAMING_SNAKE_CASE						)
    targs.append(_SCREAMING_SNAKE_CASE						)
  lowercase__     ,       lowercase__           =    torch.cat(_SCREAMING_SNAKE_CASE						), torch.cat(_SCREAMING_SNAKE_CASE						)
  return logits, targs




def      __UpperCamelCase    (_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=82						,		_SCREAMING_SNAKE_CASE=False						,		_SCREAMING_SNAKE_CASE=False						,		_SCREAMING_SNAKE_CASE=16						)							->  Dict:
  lowercase__     ,       lowercase__     ,       lowercase__           =    get_basic_setup(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						)
  lowercase__     ,       lowercase__           =    generate_predictions(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						)
  assert (
      len(_SCREAMING_SNAKE_CASE						) == num_samples
  ), F"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_SCREAMING_SNAKE_CASE						)}"""




def      __UpperCamelCase    (_SCREAMING_SNAKE_CASE = False						,		_SCREAMING_SNAKE_CASE = False						)							->  int:
  lowercase__           =    evaluate.load('glue'						,		'mrpc'						)
  lowercase__     ,       lowercase__           =    get_mrpc_setup(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						)
  # First do baseline
  lowercase__     ,       lowercase__     ,       lowercase__           =    setup['no']
  model.to(_SCREAMING_SNAKE_CASE						)
  model.eval()
  for batch in dataloader:
    batch.to(_SCREAMING_SNAKE_CASE						)
    with torch.inference_mode():
      lowercase__           =    model(**_SCREAMING_SNAKE_CASE						)
    lowercase__           =    outputs.logits.argmax(dim=-1						)
    metric.add_batch(predictions=_SCREAMING_SNAKE_CASE						,		references=batch['labels']						)
  lowercase__           =    metric.compute()

  # Then do distributed
  lowercase__     ,       lowercase__     ,       lowercase__           =    setup['ddp']
  model.eval()
  for batch in dataloader:
    with torch.inference_mode():
      lowercase__           =    model(**_SCREAMING_SNAKE_CASE						)
    lowercase__           =    outputs.logits.argmax(dim=-1						)
    lowercase__           =    batch['labels']
    lowercase__     ,       lowercase__           =    accelerator.gather_for_metrics((preds, references)						)
    metric.add_batch(predictions=_SCREAMING_SNAKE_CASE						,		references=_SCREAMING_SNAKE_CASE						)
  lowercase__           =    metric.compute()

  for key in "accuracy f1".split():
    assert math.isclose(
        baseline[key]						,		distributed[key]						), F"""Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"""




def      __UpperCamelCase    ()							->  Union[str, Any]:
  lowercase__           =    Accelerator(split_batches=_SCREAMING_SNAKE_CASE						,		dispatch_batches=_SCREAMING_SNAKE_CASE						)
  if accelerator.is_local_main_process:
    datasets.utils.logging.set_verbosity_warning()
    transformers.utils.logging.set_verbosity_warning()
  else:
    datasets.utils.logging.set_verbosity_error()
    transformers.utils.logging.set_verbosity_error()
  # These are a bit slower so they should only be ran on the GPU or TPU
  if torch.cuda.is_available() or is_tpu_available():
    if accelerator.is_local_main_process:
      print('**Testing gather_for_metrics**'						)
    for split_batches in [True, False]:
      for dispatch_batches in [True, False]:
        if accelerator.is_local_main_process:
          print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"""						)
        test_mrpc(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						)
        accelerator.state._reset_state()
  if accelerator.is_local_main_process:
    print('**Test torch metrics**'						)
  for split_batches in [True, False]:
    for dispatch_batches in [True, False]:
      lowercase__           =    Accelerator(split_batches=_SCREAMING_SNAKE_CASE						,		dispatch_batches=_SCREAMING_SNAKE_CASE						)
      if accelerator.is_local_main_process:
        print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""						)
      test_torch_metrics(_SCREAMING_SNAKE_CASE						,		99						)
      accelerator.state._reset_state()
  if accelerator.is_local_main_process:
    print('**Test last batch is not dropped when perfectly divisible**'						)
  lowercase__           =    Accelerator()
  test_torch_metrics(_SCREAMING_SNAKE_CASE						,		512						)
  accelerator.state._reset_state()




def      __UpperCamelCase    (_SCREAMING_SNAKE_CASE						)							->  int:
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
      main()



"""simple docstring"""



import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__:Tuple									=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:Optional[int]									=			{
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class  snake_case__      (       snake_case_       ):
 _snake_case       :					Union[str, Any]							  =						"""sew-d"""


 def __init__( self    ,		lowerCamelCase=32    ,		lowerCamelCase=768    ,		lowerCamelCase=12    ,		lowerCamelCase=12    ,		lowerCamelCase=3072    ,		lowerCamelCase=2    ,		lowerCamelCase=512    ,		lowerCamelCase=256    ,		lowerCamelCase=True    ,		lowerCamelCase=True    ,		lowerCamelCase=("p2c", "c2p")    ,		lowerCamelCase="layer_norm"    ,		lowerCamelCase="gelu_python"    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.0    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.02    ,		lowerCamelCase=1E-7    ,		lowerCamelCase=1E-5    ,		lowerCamelCase="group"    ,		lowerCamelCase="gelu"    ,		lowerCamelCase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)    ,		lowerCamelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)    ,		lowerCamelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)    ,		lowerCamelCase=False    ,		lowerCamelCase=128    ,		lowerCamelCase=16    ,		lowerCamelCase=True    ,		lowerCamelCase=0.05    ,		lowerCamelCase=10    ,		lowerCamelCase=2    ,		lowerCamelCase=0.0    ,		lowerCamelCase=10    ,		lowerCamelCase=0    ,		lowerCamelCase="mean"    ,		lowerCamelCase=False    ,		lowerCamelCase=False    ,		lowerCamelCase=256    ,		lowerCamelCase=0    ,		lowerCamelCase=1    ,		lowerCamelCase=2    ,		**lowerCamelCase    ,		):
        super().__init__(**lowerCamelCase    ,		pad_token_id=lowerCamelCase    ,		bos_token_id=lowerCamelCase    ,		eos_token_id=lowerCamelCase						)
        __a       =    hidden_size
        __a       =    feat_extract_norm
        __a       =    feat_extract_activation
        __a       =    list(lowerCamelCase						)
        __a       =    list(lowerCamelCase						)
        __a       =    list(lowerCamelCase						)
        __a       =    conv_bias
        __a       =    num_conv_pos_embeddings
        __a       =    num_conv_pos_embedding_groups
        __a       =    len(self.conv_dim						)
        __a       =    num_hidden_layers
        __a       =    intermediate_size
        __a       =    squeeze_factor
        __a       =    max_position_embeddings
        __a       =    position_buckets
        __a       =    share_att_key
        __a       =    relative_attention
        __a       =    norm_rel_ebd
        __a       =    list(lowerCamelCase						)
        __a       =    hidden_act
        __a       =    num_attention_heads
        __a       =    hidden_dropout
        __a       =    attention_dropout
        __a       =    activation_dropout
        __a       =    feat_proj_dropout
        __a       =    final_dropout
        __a       =    layer_norm_eps
        __a       =    feature_layer_norm_eps
        __a       =    initializer_range
        __a       =    vocab_size

        if (
            (len(self.conv_stride						) != self.num_feat_extract_layers)
            or (len(self.conv_kernel						) != self.num_feat_extract_layers)
            or (len(self.conv_dim						) != self.num_feat_extract_layers)
        ):
               raise ValueError(
                   "Configuration for convolutional layers is incorrect."
                   "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
                   F"but is `len(config.conv_dim) = {len(self.conv_dim						)}`, `len(config.conv_stride)"
                   F"= {len(self.conv_stride						)}`, `len(config.conv_kernel) = {len(self.conv_kernel						)}`."						)

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        __a       =    apply_spec_augment
        __a       =    mask_time_prob
        __a       =    mask_time_length
        __a       =    mask_time_min_masks
        __a       =    mask_feature_prob
        __a       =    mask_feature_length
        __a       =    mask_feature_min_masks

        # ctc loss
        __a       =    ctc_loss_reduction
        __a       =    ctc_zero_infinity

        # sequence classification
        __a       =    use_weighted_layer_sum
        __a       =    classifier_proj_size






 @property
 def 						a__   ( self						):
        return functools.reduce(operator.mul    ,		self.conv_stride    ,		1						)
